1. Joint Modeling
1.1 Trace plots for convergence check
The current MCMC setting is:
- 4000000 iteration;
- 3000000 burn-in;
- 1000 thinning.
1.2 Gelman and Rubin’s convergence check
## Potential scale reduction factors:
##
## Point est. Upper C.I.
## HDevsum 1.00 1.00
## LDevsum 1.00 1.00
## dh0 1.02 1.10
## dh1 1.02 1.10
## dh2 1.00 1.01
## dl0 1.00 1.01
## dl1 1.00 1.01
## dl2 1.00 1.00
##
## Multivariate psrf
##
## 1.02
1.3 ACF Plots
Here we plotted ACF plots for the following variables:
- Total deviance;
- Variables that didn’t pass the convergence check.
1.4 WAIC results
| LevelH | LevelL | |
|---|---|---|
| DIC | 1214.89274 | 22464.2353 |
| DIC3 | 1153.87206 | 22549.4321 |
| PWAIC | 42.65392 | 255.6775 |
| WAIC | 1180.95189 | 22572.4635 |
2. Separate Modeling of High-Level
2.1 Trace plots for convergence check
The current MCMC setting is:
- 4000000 iteration;
- 3000000 burn-in;
- 1000 thinning.
2.2 Gelman and Rubin’s convergence check
## Potential scale reduction factors:
##
## Point est. Upper C.I.
## HDevsum 1.00 1.00
## dh0 1.10 1.37
## dh1 1.08 1.31
## dh2 1.00 1.00
##
## Multivariate psrf
##
## 1.07
2.3 ACF Plots
Here we plotted ACF plots for the following variables:
- Total deviance;
- Variables that didn’t pass the convergence check.
2.4 WAIC results
| H2 | |
|---|---|
| DIC | 1351.42375 |
| DIC3 | 1239.00179 |
| PWAIC | 87.49656 |
| WAIC | 1305.84653 |
3. Separate Modeling for Low-level
3.1 Trace plots for convergence check
The current MCMC setting is:
- 4000000 iteration;
- 3000000 burn-in;
- 1000 thinning.
3.2 Gelman and Rubin’s convergence check
## Potential scale reduction factors:
##
## Point est. Upper C.I.
## LDevsum 1.000 1.000
## dl0 0.999 0.999
## dl1 1.003 1.011
## dl2 1.001 1.003
##
## Multivariate psrf
##
## 1
3.3 ACF Plots
Here we plotted ACF plots for the following variables:
- Total deviance;
- Variables that didn’t pass the convergence check.
3.4 WAIC results
| L2 | |
|---|---|
| DIC | 22586.0287 |
| DIC3 | 22660.2572 |
| PWAIC | 322.2886 |
| WAIC | 22696.0729 |